Computed tomography (CT), sometimes called computed axial tomography (CAT) or CAT scan, uses special x-ray equipment to obtain image data from different angles around a person's body, and then uses computer processing of the data to create a two-dimensional cross-sectional image (i.e., slice) of the body tissues and organs that were scanned. CT imaging is particularly useful because it can show a combination of several different types of tissue (i.e., heart, lungs, stomach, colon, kidneys, liver, bone, blood vessels, muscles, etc.) with high spatial resolution and a great deal of clarity and contrast. Radiologists can interpret CT images to diagnose various injuries and illnesses, such as cardiovascular disease, trauma, cancer, and musculoskeletal disorders. CT images can also be used to aid in minimally invasive surgeries, and to allow for accurate planning and pinpointing of tumors for radiation treatment, among other things.
CT imaging allows structures within a body to be identified and delineated without superimposing other structures on the images created thereby. In a typical CT system, an x-ray source emits a beam that passes through a section of an object being imaged, typically a patient. After passing through the object and being more or less attenuated by the object, detectors receive the beam and measure the beam's intensity, which can vary since different parts of the body absorb and attenuate the x-rays differently.
Computed tomography is particularly useful in the medical field for analyzing tortuous structures such as airways, vessels, ducts or nerves, and thanks to advances in ECG-gated reconstruction techniques, CT can now provide quality images of the heart and coronary arteries. Conventional methods and apparatuses for imaging heart and coronary arteries use multiple oblique slices to analyze local segments of these structures. Although these conventional methods and apparatuses provide clear, undistorted pictures of short sections of tortuous structures, the views rarely encompass the full length thereof. Furthermore, when looking at axial CT slices, coronary arteries are often difficult to analyze because of the anatomy of the heart, the small size of the coronary arteries, and the complexity of the 3-dimensional trajectory of the coronary arteries.
Knowing the central path of coronary vessels allows one to provide advanced visualization modes like curved or lumen reformatted views, which display, on a two dimensional screen, reformatted views along the coronary vessels based on the computed central path thereof. Knowing the central path of coronary vessels also allows easier navigation in the vessel, vessel quantification, and better 3-dimensional visualization of the vessel's anatomy. Post-processing computation (i.e., estimation) of the central path of coronary vessels is currently possible after defining an upstream starting point and a downstream ending point for each vessel branch. However, existing coronary tracking techniques are not ideal, and often they do not accurately track the coronary arteries.
Designing a robust coronary artery tracking technique is challenging for a variety of reasons. The small size of coronary arteries leads to contrast variations and partial volume effects, especially in the presence of calcifications. Additionally, the trajectory of coronary arteries is complex. Furthermore, the coronary arteries are in close proximity to heart cavities (i.e., ventricles) and veins which, although they are usually larger and more uniform than the coronary arteries, generally have a similar gray level value in a CT image as the coronary arteries. As a result, vessel tracking techniques sometimes fail to properly image the vessel, and instead provide a path that goes through a vein or cavity instead of following the desired vessel. Since existing coronary tracking techniques have many drawbacks, it would be desirable to have improved coronary tracking techniques.
This invention provides preprocessing techniques that improve the tracking and imaging of coronary arteries by increasing the robustness of a vessel tracking algorithm for cardiac CT exams.